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2 changed files with 65 additions and 4 deletions
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import sys |
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import cv |
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def hs_histogram(src): |
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# Convert to HSV |
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hsv = cv.CreateImage(cv.GetSize(src), 8, 3) |
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cv.CvtColor(src, hsv, cv.CV_BGR2HSV) |
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# Extract the H and S planes |
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h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1) |
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s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1) |
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cv.Split(hsv, h_plane, s_plane, None, None) |
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planes = [h_plane, s_plane] |
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h_bins = 30 |
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s_bins = 32 |
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hist_size = [h_bins, s_bins] |
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# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */ |
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h_ranges = [0, 180] |
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# saturation varies from 0 (black-gray-white) to |
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# 255 (pure spectrum color) |
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s_ranges = [0, 255] |
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ranges = [h_ranges, s_ranges] |
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scale = 10 |
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hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1) |
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cv.CalcHist([cv.GetImage(i) for i in planes], hist) |
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(_, max_value, _, _) = cv.GetMinMaxHistValue(hist) |
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hist_img = cv.CreateImage((h_bins*scale, s_bins*scale), 8, 3) |
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for h in range(h_bins): |
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for s in range(s_bins): |
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bin_val = cv.QueryHistValue_2D(hist, h, s) |
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intensity = cv.Round(bin_val * 255 / max_value) |
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cv.Rectangle(hist_img, |
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(h*scale, s*scale), |
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((h+1)*scale - 1, (s+1)*scale - 1), |
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cv.RGB(intensity, intensity, intensity), |
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cv.CV_FILLED) |
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return hist_img |
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def precornerdetect(image): |
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# assume that the image is floating-point |
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corners = cv.CloneMat(image) |
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cv.PreCornerDetect(image, corners, 3) |
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dilated_corners = cv.CloneMat(image) |
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cv.Dilate(corners, dilated_corners, None, 1) |
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corner_mask = cv.CreateMat(image.rows, image.cols, cv.CV_8UC1) |
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cv.Sub(corners, dilated_corners, corners) |
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cv.CmpS(corners, 0, corner_mask, cv.CV_CMP_GE) |
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return (corners, corner_mask) |
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def findstereocorrespondence(image_left, image_right): |
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# image_left and image_right are the input 8-bit single-channel images |
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# from the left and the right cameras, respectively |
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(r, c) = (image_left.rows, image_left.cols) |
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disparity_left = cv.CreateMat(r, c, cv.CV_16S) |
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disparity_right = cv.CreateMat(r, c, cv.CV_16S) |
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state = cv.CreateStereoGCState(16, 2) |
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cv.FindStereoCorrespondenceGC(image_left, image_right, disparity_left, disparity_right, state, 0) |
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return (disparity_left, disparity_right) |
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